@inproceedings{29294,
  author       = {{Nickchen, Tobias and Heindorf, Stefan and Engels, Gregor}},
  booktitle    = {{2021 IEEE Winter Conference on Applications of Computer Vision (WACV)}},
  publisher    = {{IEEE}},
  title        = {{{Generating Physically Sound Training Data for Image Recognition of Additively Manufactured Parts}}},
  doi          = {{10.1109/wacv48630.2021.00204}},
  year         = {{2021}},
}

@misc{33733,
  author       = {{Heindorf, Stefan}},
  title        = {{{Automatically generating instructions from tutorials for search and user navigation}}},
  year         = {{2021}},
}

@article{23728,
  abstract     = {{We demonstrate the integration of amorphous tungsten silicide superconducting nanowire single-photon detectors on titanium in-diffused lithium niobate waveguides. We show proof-of-principle detection of evanescently coupled photons of 1550 nm wavelength using bidirectional waveguide coupling for two orthogonal polarization directions. We investigate the internal detection efficiency as well as detector absorption using coupling-independent characterization measurements. Furthermore, we describe strategies to improve the yield and efficiency of these devices.}},
  author       = {{Höpker, Jan Philipp and Verma, Varun B and Protte, Maximilian and Ricken, Raimund and Quiring, Viktor and Eigner, Christof and Ebers, Lena and Hammer, Manfred and Förstner, Jens and Silberhorn, Christine and Mirin, Richard P and Woo Nam, Sae and Bartley, Tim}},
  issn         = {{2515-7647}},
  journal      = {{Journal of Physics: Photonics}},
  pages        = {{034022}},
  title        = {{{Integrated superconducting nanowire single-photon detectors on titanium in-diffused lithium niobate waveguides}}},
  doi          = {{10.1088/2515-7647/ac105b}},
  volume       = {{3}},
  year         = {{2021}},
}

@article{25046,
  abstract     = {{<jats:p>While increasing digitalization enables multiple advantages for a reliable operation of technical systems, a remaining challenge in the context of condition monitoring is seen in suitable consideration of uncertainties affecting the monitored system. Therefore, a suitable prognostic approach to predict the remaining useful lifetime of complex technical systems is required. To handle different kinds of uncertainties, a novel Multi-Model-Particle Filtering-based prognostic approach is developed and evaluated by the use case of rubber-metal-elements. These elements are maintained preventively due to the strong influence of uncertainties on their behavior. In this paper, two measurement quantities are compared concerning their ability to establish a prediction of the remaining useful lifetime of the monitored elements and the influence of present uncertainties. Based on three performance indices, the results are evaluated. A comparison with predictions of a classical Particle Filter underlines the superiority of the developed Multi-Model-Particle Filter. Finally, the value of the developed method for enabling condition monitoring of technical systems related to uncertainties is given exemplary by a comparison between the preventive and the predictive maintenance strategy for the use case.</jats:p>}},
  author       = {{Bender, Amelie}},
  issn         = {{2075-1702}},
  journal      = {{Machines}},
  keywords     = {{prognostics, RUL predictions, particle filter, uncertainty consideration, Multi-Model-Particle Filter, model-based approach, rubber-metal-elements, predictive maintenance}},
  number       = {{10}},
  title        = {{{A Multi-Model-Particle Filtering-Based Prognostic Approach to Consider Uncertainties in RUL Predictions}}},
  doi          = {{10.3390/machines9100210}},
  volume       = {{9}},
  year         = {{2021}},
}

@techreport{35889,
  abstract     = {{Network and service coordination is important to provide modern services consisting of multiple interconnected components, e.g., in 5G, network function virtualization (NFV), or cloud and edge computing. In this paper, I outline my dissertation research, which proposes six approaches to automate such network and service coordination. All approaches dynamically react to the current demand and optimize coordination for high service quality and low costs. The approaches range from centralized to distributed methods and from conventional heuristic algorithms and mixed-integer linear programs to machine learning approaches using supervised and reinforcement learning. I briefly discuss their main ideas and advantages over other state-of-the-art approaches and compare strengths and weaknesses.}},
  author       = {{Schneider, Stefan Balthasar}},
  keywords     = {{nfv, coordination, machine learning, reinforcement learning, phd, digest}},
  title        = {{{Conventional and Machine Learning Approaches for Network and Service Coordination}}},
  year         = {{2021}},
}

@inbook{36260,
  author       = {{Weber, Jutta}},
  booktitle    = {{Drone Imaginaries. The Power of Remote Vision}},
  editor       = {{Maurer, Kathrin and Graae, Andreas Immanuel}},
  pages        = {{167--179}},
  publisher    = {{Manchester University Press}},
  title        = {{{Artificial Intelligence and the Sociotechnical Imaginary: On Skynet, Self-Healing Swarms and Slaughterbots}}},
  year         = {{2021}},
}

@inbook{36257,
  author       = {{Weber, Jutta and Mayer, Katja}},
  booktitle    = {{Explorations in Digital Cultures}},
  editor       = {{Burkhardt, Marcus and Shnayien, Mary and Grashöfer, Katja}},
  publisher    = {{meson press}},
  title        = {{{From Optimizing Military Operations to Targeting Terrorist Networks: Social Network Analysis in Data-Driven Warfare}}},
  year         = {{2021}},
}

@inbook{36256,
  author       = {{Weber, Jutta}},
  booktitle    = {{In digitaler Gesellschaft. Neukonfigurationen zwischen Robotern, Algorithmen und Usern}},
  editor       = {{Braun, Kathrin and Kropp, Cordula}},
  pages        = {{213--222}},
  publisher    = {{transcript}},
  title        = {{{Human-Machine Learning und Digital Commons}}},
  year         = {{2021}},
}

@article{36545,
  abstract     = {{Due to the Corona crisis, German Higher Education Institutions had to close their campuses in March and lecturers had to teach online. To understand how the Corona crisis affected students, first this article explains the structural and social inequalities in the German higher education system, using Tinto's (1975; 1997) student engagement theory. Second, the concept of Bergman-Rosamond et al. (2020) is used to analyze the challenges that Corona has raised for students, including current surveys. We found that the closure of the social space campus (and the Corona crisis as a whole) particularly hit hard those students who had previously been affected by (intersectional) inequality. Therefore, to lessen the specific challenges associated with the ad hoc transition to digital studying, the creation of a digital community of learning can help. We demonstrate how such a community can be created by the example seminar, "Digital practices: an autoethnographic observation". During the seminar, students recorded their digital technology use in a journal, and we analyzed the diary entries using the collaborate autoethnography method. The seminar example shows that this method is well suited for the development of a community of learning as it not only places students in the spotlight but as students work together on a topic they get to know each other, and a basis of trust is created through peer-feedback. Therefore, it was important to have a digital space (in this case Mahara) where the exchange could take place. The continuous insight into the students’ "learning status" enabled the lecturer to promote the learning and provide individual assistance for the students.}},
  author       = {{Steinhardt, Isabel}},
  journal      = {{ISA Pedagogy Series}},
  keywords     = {{Intersectionality, inequality, gender, diversity, higher-education, crisis}},
  number       = {{1}},
  pages        = {{42--59}},
  publisher    = {{International Sociology Association}},
  title        = {{{Students in the spotlight: Using collaborative autoethnography to build a community of learning in the Corona crisis}}},
  volume       = {{1}},
  year         = {{2021}},
}

@techreport{36551,
  abstract     = {{The call for free access to research data and materials is becoming louder and louder from the political and scientific communities in Germany. More and more researchers are facing demands to open up qualitative research data for scientific purposes. They often have a general interest in sharing their data, but are unsure how to proceed. This handout was developed to provide an initial introduction to opening and sharing qualitative data. It was developed at a workshop held in Berlin in January 2020, organized by the research group „Digitization of Science“ of the Weizenbaum Institute, together with its associate researcher Dr. Isabel Steinhardt from the University of Kassel. The workshop involved staff from German research data centers as well as mentees and mentors from the Fellow Program Open Science who already have experience with Open Science, qualitative research, and interdisciplinary research. The handout is addressed primarily to qualitatively researching scientists in Germany. For this reason, it was initially written in German. One year later, we have now decided to translate the handout into English as well. The reasons are twofold: first, we want to make it accessible to researchers in Germany with little knowledge of German. Second, we also want to give interested people outside Germany an insight into the German system and the German discussion about opening up and sharing qualitative data. Due to the objectives and the history of its development, the handout focuses on the German context. This includes the literature references and further sources, and the references to research data centers as well as legal issues. We have deliberately not included a contextualization of the German situation in international discussions in order to keep the handout as short as possible.}},
  author       = {{Steinhardt, Isabel and Fischer, Caroline and Heimstädt, Maximilian and Hirsbrunner, Simon David and Ikiz-Akinci, Dilek and Kressin, Lisa and Kretzer, Susanne and Möllenkamp, Andreas and Portzelt, Maike and Rahal, Rima-Maria and Schimmler, Sonja and Wilke, René and Wünsche, Hannes}},
  pages        = {{20}},
  publisher    = {{Weizenbaum Institute for the Networked Society - The German Internet Institu}},
  title        = {{{Opening up and Sharing Data from Qualitative Research: A Primer}}},
  doi          = {{10.34669/WI.WS/17}},
  volume       = {{17}},
  year         = {{2021}},
}

@inbook{36557,
  abstract     = {{Anhand einer explorativen Studie in den Fächern Jura und Soziale Arbeit wird rekonstruiert welche Praktiken Studierende in Bezug auf digitale Technologien haben und ob digitale Praktiken im Studium existieren. Dazu wurden narrative Interviews mit sechs Studierenden geführt, die habitushermeneutisch ausgewertet wurden. Die Ergebnisse zeigen keine digitalen Praktiken in Bezug auf das Studium, wohingegen sich digitale Praktiken im alltäglichen Leben zeigen. Für das Studium zeigen sich unterschiedliche Praktiken in der Nutzung digitaler Technologien, die in Beziehung zu den Kapitalsorten stehen, die Studierende besitzen. Die explorativen Ergebnisse legen nahe, dass Studierende unterschiedliche Hilfestellungen in Bezug auf die Digitalisierung des Studiums benötigen, die in der Lehrplanung und -pädagogik berücksichtigt werden müssten.}},
  author       = {{Steinhardt, Isabel}},
  booktitle    = {{Entwicklungen im Feld der Hochschule}},
  editor       = {{Bremer, Helmut and Lange-Vester, Andrea}},
  isbn         = {{978-3-7799-5861-1}},
  pages        = {{213--226}},
  title        = {{{Digitale Praktiken und das Studium}}},
  year         = {{2021}},
}

@misc{30961,
  author       = {{Lammer, Christina}},
  publisher    = {{www.kibum.de (Thomas Boyken und Jörn Brüggemann),}},
  title        = {{{Andrea Karimé: Sterne im Kopf und ein unglaublicher Plan. Köln: Peter Hammer Verlag 2021}}},
  year         = {{2021}},
}

@article{25227,
  abstract     = {{<jats:title>Abstract</jats:title><jats:p>Quantum well (QW) heterostructures have been extensively used for the realization of a wide range of optical and electronic devices. Exploiting their potential for further improvement and development requires a fundamental understanding of their electronic structure. So far, the most commonly used experimental techniques for this purpose have been all-optical spectroscopy methods that, however, are generally averaging in momentum space. Additional information can be gained by angle-resolved photoelectron spectroscopy (ARPES), which measures the electronic structure with momentum resolution. Here we report on the use of extremely low-energy ARPES (photon energy ~ 7 eV) to increase depth sensitivity and access buried QW states, located at 3 nm and 6 nm below the surface of cubic-GaN/AlN and GaAs/AlGaAs heterostructures, respectively. We find that the QW states in cubic-GaN/AlN can indeed be observed, but not their energy dispersion, because of the high surface roughness. The GaAs/AlGaAs QW states, on the other hand, are buried too deep to be detected by extremely low-energy ARPES. Since the sample surface is much flatter, the ARPES spectra of the GaAs/AlGaAs show distinct features in momentum space, which can be reconducted to the band structure of the topmost surface layer of the QW structure. Our results provide important information about the samples’ properties required to perform extremely low-energy ARPES experiments on electronic states buried in semiconductor heterostructures.</jats:p>}},
  author       = {{Hajlaoui, Mahdi and Ponzoni, Stefano and Deppe, Michael and Henksmeier, Tobias and As, Donat Josef and Reuter, Dirk and Zentgraf, Thomas and Springholz, Gunther and Schneider, Claus Michael and Cramm, Stefan and Cinchetti, Mirko}},
  issn         = {{2045-2322}},
  journal      = {{Scientific Reports}},
  title        = {{{Extremely low-energy ARPES of quantum well states in cubic-GaN/AlN and GaAs/AlGaAs heterostructures}}},
  doi          = {{10.1038/s41598-021-98569-6}},
  volume       = {{11}},
  year         = {{2021}},
}

@inproceedings{44843,
  abstract     = {{Unsupervised blind source separation methods do not require a training phase
and thus cannot suffer from a train-test mismatch, which is a common concern in
neural network based source separation. The unsupervised techniques can be
categorized in two classes, those building upon the sparsity of speech in the
Short-Time Fourier transform domain and those exploiting non-Gaussianity or
non-stationarity of the source signals. In this contribution, spatial mixture
models which fall in the first category and independent vector analysis (IVA)
as a representative of the second category are compared w.r.t. their separation
performance and the performance of a downstream speech recognizer on a
reverberant dataset of reasonable size. Furthermore, we introduce a serial
concatenation of the two, where the result of the mixture model serves as
initialization of IVA, which achieves significantly better WER performance than
each algorithm individually and even approaches the performance of a much more
complex neural network based technique.}},
  author       = {{Boeddeker, Christoph and Rautenberg, Frederik and Haeb-Umbach, Reinhold}},
  booktitle    = {{ITG Conference on Speech Communication}},
  location     = {{Kiel}},
  title        = {{{A Comparison and Combination of Unsupervised Blind Source Separation  Techniques}}},
  year         = {{2021}},
}

@inproceedings{28259,
  author       = {{Boeddeker, Christoph and Zhang, Wangyou and Nakatani, Tomohiro and Kinoshita, Keisuke and Ochiai, Tsubasa and Delcroix, Marc and Kamo, Naoyuki and Qian, Yanmin and Haeb-Umbach, Reinhold}},
  booktitle    = {{ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}},
  title        = {{{Convolutive Transfer Function Invariant SDR Training Criteria for Multi-Channel Reverberant Speech Separation}}},
  doi          = {{10.1109/icassp39728.2021.9414661}},
  year         = {{2021}},
}

@inproceedings{23998,
  author       = {{Schmalenstroeer, Joerg and Heitkaemper, Jens and Ullmann, Joerg and Haeb-Umbach, Reinhold}},
  booktitle    = {{29th European Signal Processing Conference (EUSIPCO)}},
  pages        = {{1--5}},
  title        = {{{Open Range Pitch Tracking for Carrier Frequency Difference Estimation from HF Transmitted Speech}}},
  year         = {{2021}},
}

@misc{48787,
  author       = {{Hartung, Olaf}},
  booktitle    = {{H-Soz-Kult}},
  publisher    = {{Clio-online - Historisches Fachinformationssystem e.V.}},
  title        = {{{Rezension von: Jörg van Norden, Thomas Must, Lars Deile, Peter Riedel, Susan Krause und Wanda Schürenberg (Hgg.): Geschichtsdidaktische Grundbegriffe. Ein Bilderbuch für Studium, Lehre und Beruf. Hannover 2020}}},
  year         = {{2021}},
}

@article{22528,
  abstract     = {{Due to the ad hoc nature of wireless acoustic sensor networks, the position of the sensor nodes is typically unknown. This contribution proposes a technique to estimate the position and orientation of the sensor nodes from the recorded speech signals. The method assumes that a node comprises a microphone array with synchronously sampled microphones rather than a single microphone, but does not require the sampling clocks of the nodes to be synchronized. From the observed audio signals, the distances between the acoustic sources and arrays, as well as the directions of arrival, are estimated. They serve as input to a non-linear least squares problem, from which both the sensor nodes’ positions and orientations, as well as the source positions, are alternatingly estimated in an iterative process. Given one set of unknowns, i.e., either the source positions or the sensor nodes’ geometry, the other set of unknowns can be computed in closed-form. The proposed approach is computationally efficient and the first one, which employs both distance and directional information for geometry calibration in a common cost function. Since both distance and direction of arrival measurements suffer from outliers, e.g., caused by strong reflections of the sound waves on the surfaces of the room, we introduce measures to deemphasize or remove unreliable measurements. Additionally, we discuss modifications of our previously proposed deep neural network-based acoustic distance estimator, to account not only for omnidirectional sources but also for directional sources. Simulation results show good positioning accuracy and compare very favorably with alternative approaches from the literature.}},
  author       = {{Gburrek, Tobias and Schmalenstroeer, Joerg and Haeb-Umbach, Reinhold}},
  issn         = {{1687-4722}},
  journal      = {{EURASIP Journal on Audio, Speech, and Music Processing}},
  title        = {{{Geometry calibration in wireless acoustic sensor networks utilizing DoA and distance information}}},
  doi          = {{10.1186/s13636-021-00210-x}},
  year         = {{2021}},
}

@inproceedings{23994,
  author       = {{Gburrek, Tobias and Schmalenstroeer, Joerg and Haeb-Umbach, Reinhold}},
  booktitle    = {{ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}},
  title        = {{{Iterative Geometry Calibration from Distance Estimates for Wireless Acoustic Sensor Networks}}},
  doi          = {{10.1109/icassp39728.2021.9413831}},
  year         = {{2021}},
}

@inproceedings{23999,
  author       = {{Gburrek, Tobias and Schmalenstroeer, Joerg and Haeb-Umbach, Reinhold}},
  booktitle    = {{Speech Communication; 14th ITG-Symposium}},
  pages        = {{1--5}},
  title        = {{{On Source-Microphone Distance Estimation Using Convolutional Recurrent Neural Networks}}},
  year         = {{2021}},
}